• Computers and Concrete
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• 제31권2호
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2001년도 봄 학술발표회 논문집
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• pp.189-194
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• 2001

In this study, ultimate strength of concrete deep beams with an opening is predicted by using Strut-and-Tie Model with a new effective compressive strength. First crack occurs around an opening by stress concentration due to geometric discontinuity. This results in decreasing ultimate strength of deep beams with an opening compared with general deep beams. With fundamental notion that ultimate strength of deep beam with an opening decreases as a result of reduction in effective compressive strength of a concrete strut, an equivalent effective compressive strength formula is proposed in order to reflect ultimate strength reduction due to an opening located in a concrete strut. An equivalent effective compressive strength formula which can reflect opening size and position is added to a testified algorithm of predicting ultimate strength of concrete deep beams. Therefore, ultimate strength of concrete deep beam with an opening is predicted by using a simple and rational STM algorithm including an equivalent effective compressive strength formula, not by finite element analysis or a former complex Strut-and-Tie Model

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2005년도 봄학술 발표회 논문집(II)
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• pp.105-108
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• 2005

This study investigated fundamental properties of the recycled aggregate which was produced through recent hi-techniques of recycling. In addition, the mechanical properties of the concrete that used the recycled aggregate were compared to the concrete used the natural aggregate. From the results of the mechanical property tests, as the recycled aggregate replacement ratio increased, the compressive strength and elastic modulus decreased. When the recycled aggregate completely replaced the natural aggregate, the compressive strength and elastic modulus was about 15$\%$ and 35$\%$ lower than the natural aggregate concrete, respectively. Based on the test results, equations for prediction of compressive strength and elastic modulus were suggested in the consideration of the amount of the replaced recycled aggregate. Based on the test results and study, the equation predicting the required development length of the recycled aggregate concrete is proposed.

• 한국주거학회:학술대회논문집
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• 한국주거학회 2008년도 춘계학술발표대회 논문집
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• pp.399-404
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• 2008

The objective of this study is to develop the predicting method of concrete strength when remove concrete form-work without making cement test piece at construction site. For this purpose, this study catches the Maturity Method by using RFID, the usability of which is now being emphasized at site, accumulates and record the strength data, which can be gained with the results of existing Maturity Method method that is accompanied with strength estimation study, in database, and finally proposes the system structure which can check the estimated strength by Maturity Method. The merits of this method by using of Maturity Method are as follows; More objective, precise, and rapid decision can be made to the concrete strength and about the maintaining period of concrete form and form support. More efficient control of integrated material management system can be possible. Architectural field example using RFID can be suggested more concretely. RFID applicability can be extended by using DB of material integration management system.

• Structural Engineering and Mechanics
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• 제58권6호
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• pp.989-999
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• 2016

Moist curing of concrete is a time consuming procedure. It takes minimum 28 days of curing to obtain the characteristic strength of concrete. However, under certain situations such as shortage of time, weather conditions, on the spot changes in project and speedy construction, waiting for entire curing period becomes unaffordable. This situation demands early strength of concrete which can be met using accelerated curing methods. It becomes necessary to obtain early strength of concrete rather than waiting for entire period of curing which proves to be uneconomical. In India, accelerated curing methods are used to arrive upon the actual strength by resorting to the equations suggested by Bureau of Indian Standards' (BIS). However, it has been observed that the results obtained using above equations are exaggerated. In the present experimental investigations, the results of the accelerated compressive strength of the concrete are used to develop the regression models for predicting the short term and long term compressive strength of concrete. The proposed regression models show better agreement with the actual compressive strength than the existing model suggested by BIS specification.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2003년도 봄 학술발표회 논문집
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• pp.896-901
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• 2003

This paper describes a model on shear strength of RC columns strengthened with FRP sheets. In this study, we propose a confined concrete strength model of RC columns confined by transverse reinforcement as well as FRP sheet by introducing corresponding effective confinement coefficient for each confined concrete area. Then, a shear strength model of the confined RC columns is proposed by lower and upper bound limit analysis which are based on the truss-arch model theory and shear band failure theory, respectively. Along with shear test data obtained from strengthened column specimens, the developed analytical models are verified. The comparison shows that the proposed model can be used effectively for the prediction of both ultimate strength and required amount of strengthening in retrofit design for RC columns.

• 한국구조물진단유지관리공학회 논문집
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• 제8권2호
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• pp.159-167
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• 2004

콘크리트의 압축강도는 콘크리트를 생산하는 기준으로 사용된다. 콘크리트 압축강도 시험은 복잡하고 시간이 걸리는 일이고, 보통 건설현장에서 타설 후 28일 후에 실행되기 때문에, 시험결과가 필요강도를 만족하지 않을 경우에 구조물의 시공에 문제를 초래할 수도 있다. 따라서, 콘크리트 타설 전에 강도를 예측하는 것이 요구되고 있다. 본 연구에서는 콘크리트 배합비를 기초로 하여 콘크리트 압축강도를 예측하기 위한 확률론적 방법을 제시하였다. 패턴인식 분야에서 많이 활용되어온 확률신경망 기법을 활용하여 콘크리트 압축강도 추정을 수행하였다. 콘크리트 압축강도 시험결과를 활용하여 확률신경망 기법의 적용성을 검증하였으며, 실제 시험결과와 비교를 수행하였다. 비교결과, 본 연구에서 제시된 확률신경망을 활용한 콘크리트 압축강도 추정기법이 콘크리트의 압축강도를 확률적으로 추정하는데 매우 효과적으로 적용될 수 있음을 확인하였다.

• Computers and Concrete
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• 제22권2호
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• 콘크리트학회논문집
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• 제16권1호
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• pp.102-110
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• 2004

최근 시멘트 및 골재 등 원재료 값의 상승 및 세계적인 유가 급등으로 인한 운송비의 증가로 레미콘 제조원가는 상승하고있다. 그러나 레미콘 제조업체들 간의 과당경쟁으로 인해 레미콘의 납품 단가는 오히려 낮아지고 있는 실정이다. 이를 극복하기 위한 일환으로 레미콘 제조업체들은 레미콘의 제조원가를 최소한으로 줄이고자 하는 노력 중 하나로 고로슬래그미분말 및 플라이애쉬를 혼화재로 사용하는 업체가 증가하고 있다. 그러나 이러한 광물질 혼화재를 사용한 콘크리트의 품질관리에 대한 연구는 미흡한 실정이다. 따라서, 본 연구에서는 고로슬래그미분말 혼합 콘크리트의 28일 압축강도를 조기에 예측하기 위해 열수양생법 및 표준양생에 의한 7일 압축강도를 이용하였다. 고로슬래그미분말 혼합률 별로 선형회귀분석을 실시하여 추정식을 제시하였고 90%의 신뢰구간을 나타내었다. 또한 실험의 신뢰성을 높이기 위해 모든 배합은 3회 반복하였고, 배합순서는 랜덤추출법을 사용하였다. 이러한 실험결과 열수양생법에 의한 1일 촉진강도로서 고로슬래그미분말 혼합 콘크리트의 재령 28일 압축강도를 예측할 수 있는 추정식의 신뢰성을 확인하는 성과를 얻었다.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 가을 학술발표회 논문집
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• pp.542-549
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• 2004

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network (PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Adaptive probabilistic neural network (APNN) was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment algorithm. The conventional PNN and APNN were applied to predict the compressive strength of concrete using actual test data of a concrete company. APNN showed better results than the conventional PNN in predicting the compressive strength of concrete.